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Psychological model of particle swarm optimization based multiple emotions

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Abstract

This paper proposes a novel approach to swarm particle optimization based on emotional behavior to solve real optimization problems. In the trend of PSO manipulating self-adaptive control to regulate potential parameters, the proposed algorithm involves both a semi-adaptive inertia weight and an emotional factor at the level of the velocity rule. The semi-inertia weight highlights a specific comportment. Thus, due to the few changes occurred in its adaptive “life”, it continues to evolve with a significantly smaller constant for the benefit of a finer exploitation. The emotion factor presents an important feature of convergence because it splits up the search space into potential regions that are finely explored by sub-swarm populations with the same emotions. The principle of particles with multiple emotions intended for the categorization of particles into specific emotional classes. The idea behind this principle is to divide to conquer, and due to presence of multiple emotional classes the multidimensional search space is widely explored at the search of the best position. Emotional PSO is evaluated on the test suit of 25 functions designed for the special session on real optimization of CEC 2005, and its performances are compared to the best algorithm the restart CMA-ES.

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References

  1. Angeline P (1998a) Evolutionary optimization versus particle swarm optimization: philosophy and performance difference. In: Proceedings of the seventh annual conference on evolutionary programming, pp 601–610

    Chapter  Google Scholar 

  2. Angeline P (1998b) Using selection to improve particle swarm optimization. In: Proceedings of the IEEE conference on evolutionary computation, pp 84–89

    Google Scholar 

  3. Auger A, Kern S, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: IEEE congress on evolutionary computation, pp 1769–1776

    Chapter  Google Scholar 

  4. Clerc M (1999) The swarm and the Queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the congress on evolutionary computation, vol 3. IEEE Press, New York, pp 1951–1957

    Google Scholar 

  5. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimentional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  6. Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 84–88

    Google Scholar 

  7. Esquivel SC, Coello Coello CA (2003) On the use of particle swarm optimization with multi modal functions. In: Proceedings of the congress on evolutionary computation, pp 1130–1136

    Google Scholar 

  8. Ge Y, Rubo Z (2005) AN emotional particle swarm algorithm. In: Advances in natural computation. LNCS, vol 3612, pp 553–561

    Chapter  Google Scholar 

  9. Guidelines (2005). http://www.ntu.edu.sg/home/EPNSugan

  10. Higashi N, Iba H (2003) Particle swarm optimization with Gaussian mutation. In: Proceedings of the IEEE swarm intelligence symposium, pp 72–79

    Google Scholar 

  11. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proc IEEE international conference on neural networks, Piscataway, NJ, pp 1942–1948

    Chapter  Google Scholar 

  12. Kennedy J, Eberhart RC (2001) Swarm intelligence. Morgan Kaufmann, San Mateo

    Google Scholar 

  13. Krink T, Lovbjerg M (2002a) The lifecycle model: combining particle swarm optimization, genetic algorithms and hill climbing. In: Proceedings of parallel problem solving from nature, pp 621–630

    Google Scholar 

  14. Krink T, Vesterstrøm JS, Riget J (2002b) Particle swarm optimization with spatial particle extension. In: Proceedings of the fourth congress on evolutionary computation

    Google Scholar 

  15. Lovbjerg M, Rasmussen TK, Krink T (2001) Hybrid particle swarm optimizer with breeding and subpopulation. In: Proceedings of the third conference on genetic and evolutionary computation, pp 469–476

    Google Scholar 

  16. Monson CK, Seppi KD (2006) Adaptive diversity in PSO. In: Proceedings of the 8th annual conference on genetic and evolutionary computation, Seattle, Washington, USA, pp 59–66

    Chapter  Google Scholar 

  17. Li N, Qin Y-Q, Sun D-B, Tong Z (2004) Particle swarm optimization with mutation operator. In: Proc international conference on machine learning and cybernetics, vol 4, pp 2251–2256

    Google Scholar 

  18. Ozcan E, Mohan CK (1998) Analysis of a simple particle swarm optimization system. In: Intelligent engineering systems through artificial neural networks, pp 253–258

    Google Scholar 

  19. Ozcan E, Mohan CK (1999) Particle swarm optimization: surfing the waves. In: Proceedings of the IEEE congress on evolutionary computation, Washington, DC, USA

    Google Scholar 

  20. Ratnaweera A, Halgamuge S, Watson H (2002) Particle swarm optimization with self-adaptive acceleration coefficients. In: Proceedings of the 1st international conference on fuzzy systems and knowledge discovery, pp 264–268

    Google Scholar 

  21. Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proc IEEE congress on evolutionary computation, pp 69–73

    Google Scholar 

  22. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. In: Proc IEEE congress on evolutionary computation, pp 1945–1950

    Google Scholar 

  23. Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proc IEEE congress on evolutionary computation, pp 101–106

    Google Scholar 

  24. Stacey A, Jancic M, Grundy I (2003) Particle swarm optimization. In: Proceedings of the congress on evolutionary computation, pp 1425–1430

    Google Scholar 

  25. Van den Bergh F, Engelbrecht AP (2004) A comparative approach to particle swarm optimization. IEEE Trans Evol Comput 8:225–239

    Article  Google Scholar 

  26. Van den Bergh F, Engelbrecht AP (2006) A study of particle swarm optimization particle trajectories. Inf Sci 176(8):937–971

    Article  MATH  Google Scholar 

  27. Wukmir WJ (1967) Emoción y Sufrimiento. BUL Labor, Barcelona

    Google Scholar 

  28. Wang W, Wang Z, Gu X, Zheng S (2009) Emotional particle swarm optimization. In: International conference on emerging intelligent computing technology and applications. LNCS, vol 575, pp 766–775

    Chapter  Google Scholar 

  29. Zhang W, Xie X (2003) DEPSO: hybrid particle swarm with differential evolution operator. In: IEEE international conference on systems, man and cybernetics, Washington, pp 3816–3821

    Google Scholar 

Download references

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Correspondence to Yamina Mohamed Ben Ali.

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Mohamed Ben Ali, Y. Psychological model of particle swarm optimization based multiple emotions. Appl Intell 36, 649–663 (2012). https://doi.org/10.1007/s10489-011-0282-3

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